Ranges in production forecast provide critical information for reservoir management decisions. Well developed methodologies exist for handling subsurface uncertainties for new field developments. The task is more challenging for fields that have been produced for several years as all models need to be conditioned to available production data in order to deliver reliable predictions. The computing cost associated with the exhaustive search of models that reproduce historical data is in general prohibitive.
Mathematically speaking, the goal of any history-matching procedure is to find the minimum of an objective function that measures the misfit between actual and simulated data. Determining the appropriate set of parameters is usually a daunting task because of the high dimensionality of the problem parameters and the non-linear relationship between the parameters and the objective function. In some instances sampling strategies based on traditional or more elaborate experimental design techniques are sufficient to build accurate proxies on which multiple solutions to the history matching problem can be identified. If the constraints in time and computer nodes are very stringent, this is often one of the only practical solutions.
Initial efforts towards computer assisted history matching focused on the calculation of sensitivity of flow responses with respect to reservoir properties. The performance of such gradient based algorithms degrades with the size of the problem and strongly depends on the initial guess of the solution vector. The recent availability of affordable computer clusters has sparked a revival in the application of greedier techniques and rigorous frameworks have been proposed for a better assessment of uncertainty in production forecasts. Evolutionary Strategies have very attractive global convergence properties and take full advantage of the scalability introduced by computer clusters. In particular, Genetic Algorithms (GAs) have been successfully used as global search engines in a variety of optimization problems including optimization of well location and trajectory, development plan, cycling steam oil production and production data integration.
There is a need to increase the computational efficiency of history matching in methods employing proxies. The present invention addresses this need.